In-class Exercise 5

A short description of the post.

Important to set fig.retina to be 3 in order for subsequent visualization to be sharp.

Installing and Loading the R package

• maptools is for creating spatial objectes
• sf is for importing geospatialdata
• raster is for creating raster object
• spatstat is for performing Spatial Point Pattern Analysis (SPPA)
• tmap is for plotting thematic maps
• tidyverse is for data handling

packages <- c('maptools','sf','raster','spatstat','tmap','tidyverse')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
}
library(p, character.only=T)

Import the Geospatial Data

The code chunk import shapefile using st_read() of sf package. The output object is in tibble sf object class.

Pgl_sf and mpsz_sf geospatial data sets into RStudio.

Note: - Do not paste directory in dsn

• No extension require as this is a SHP file

Pgl_sf <- st_read(dsn="data/shapefile", layer="Punggol_St")

Reading layer `Punggol_St' from data source 
  `C:\Users\User\Desktop\IS415\lye-jia-wei\IS415_blog\_posts\2021-09-13-in-class-exercise-5\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 2642 features and 2 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 34038.56 ymin: 40941.11 xmax: 38882.85 ymax: 44801.27
Projected CRS: SVY21 / Singapore TM

mpsz_sf <- st_read(dsn="data/shapefile",layer="MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\Users\User\Desktop\IS415\lye-jia-wei\IS415_blog\_posts\2021-09-13-in-class-exercise-5\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

Projection is SVY21

Import aspatial data from rds folder

read_rds() of readr package is used instead of readRDS() of base R is used. This is because output of read_rds() is in tibble object.

Note:

• No need to set path

• Note that there are some data issue in childcare data frame with the Lat and Lng column, the columns should be in numeric data type. The coordinate fields are in decimal degrees. Hence, wgs referencing system is assumed.

childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")

Converting the aspatial data frame into sf objects

CHAS_sf <- st_as_sf(CHAS, coords = c("X_COORDINATE","Y_COORDINATE"),crs=3414)


childcare_sf <- st_as_sf(childcare, coords= c("Lng","Lat"),crs=4326) %>% st_transform(crs=3414)

Note:

• st_as_sf() accept coordinates in character data type.

• No significant difference whether coordinate data type is change

Plotting for Reviewing

Alpha is to set transparency

tmap_mode("view")
tm_shape(childcare_sf)+tm_dots(alpha= 0.4, col="blue", size=0.05)+ tm_shape(CHAS_sf)+tm_dots(alpha= 0.4, col="red", size=0.05)

Note:

• childcare is aspatial, need to use childcare_sf

Geospatial Data Wrangling

Converting from sf to Spatial* data frame

Polygon will be converted so spatial polygon object, point will be converted to spatial point.

childcare <- as_Spatial(childcare_sf)
CHAS <- as_Spatial(CHAS_sf)
mpsz <- as_Spatial(mpsz_sf)

Converting Spatial* data frame into Spatial* Objects